NBER WORKING PAPER SERIES
MEASURING THE EFFECTS OF MONETARY POLICY:
A FACTOR-AUGMENTED VECTOR
AUTOREGRESSIVE (FAVAR) APPROACH
Ben S. Bernanke
Jean Boivin
Piotr Eliasz
Working Paper 10220
http://www.nber.org/papers/w10220
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
January 2004
Thanks to Christopher Sims, Mark Watson, Tao Zha and participants at the 2003 NBER Summer Institute
for useful comments. Boivin would like to thank National Science Foundation for financial support (SES-
0214104). The views expressed herein are those of the authors and not necessarily those of the National
Bureau of Economic Research.
©2003 by Ben S. Bernanke, Jean Boivin, and Piotr Eliasz. All rights reserved. Short sections of text, not to
exceed two paragraphs, may be quoted without explicit permission provided that full credit, including ©
notice, is given to the source.
Measuring the Effects of Monetary Policy:
A Factor-Augmented Vector Autoregressive (FAVAR) Approach
Ben S. Bernanke, Jean Boivin, and Piotr Eliasz
NBER Working Paper No. 10220
January 2004
JEL No. E3, E4, E5, C3
ABSTRACT
Structural vector autoregressions (VARs) are widely used to trace out the effect of monetary policy
innovations on the economy. However, the sparse information sets typically used in these empirical
models lead to at least two potential problems with the results. First, to the extent that central banks
and the private sector have information not reflected in the VAR, the measurement of policy
innovations is likely to be contaminated. A second problem is that impulse responses can be
observed only for the included variables, which generally constitute only a small subset of the
variables that the researcher and policymaker care about. In this paper we investigate one potential
solution to this limited information problem, which combines the standard structural VAR analysis
with recent developments in factor analysis for large data sets. We find that the information that our
factor-augmented VAR (FAVAR) methodology exploits is indeed important to properly identify the
monetary transmission mechanism. Overall, our results provide a comprehensive and coherent
picture of the effect of monetary policy on the economy.
Ben Bernanke
Board of Governors of the Federal Reserve
20
th
Street and Constitution Avenue, NW
Washington, DC 20551
ben.s.bernanke@frb.gov
Jean Boivin
Columbia Business School
Uris Hall, Room 719
3022 Broadway
New York, NY 10027-6902
and NBER
jb903@columbia.edu
Piotr Eliasz
Princeton University
001 Fisher Hall
Princeton, NJ 08544
peliasz@princeton.edu
1
1. Introduction
Since Bernanke and Blinder (1992) and Sims (1992), a considerable literature has
developed that employs vector autoregression (VAR) methods to attempt to identify and
measure the effects of monetary policy innovations on macroeconomic variables (see
Christiano, Eichenbaum, and Evans, 2000, for a survey). The key insight of this
approach is that identification of the effects of monetary policy shocks requires only a
plausible identification of those shocks (for example, as the unforecasted innovation of
the federal funds rate in Bernanke and Blinder, 1992) and does not require identification
of the remainder of the macroeconomic model. These methods generally deliver
empirically plausible assessments of the dynamic responses of key macroeconomic
variables to monetary policy innovations, and they have been widely used both in
assessing the empirical fit of structural models (see, for example, Boivin and Giannoni,
2003; Christiano, Eichenbaum, and Evans, 2001) and in policy applications.
The VAR approach to measuring the effects of monetary policy shocks appears to
deliver a great deal of useful structural information, especially for such a simple method.
Naturally, the approach does not lack for criticism. For example, researchers have
disagreed about the appropriate strategy for identifying policy shocks (Christiano,
Eichenbaum, and Evans, 2000, survey some of the alternatives; see also Bernanke and
Mihov, 1998). Alternative identifications of monetary policy innovations can, of course,
lead to different inferences about the shape and timing of the responses of economic
variables. Another issue is that the standard VAR approach addresses only the effects of
unanticipated changes in monetary policy, not the arguably more important effects of the
2
systematic portion of monetary policy or the choice of monetary policy rule (Sims and
Zha, 1998; Cochrane, 1996; Bernanke, Gertler, and Watson, 1997).
Several criticisms of the VAR approach to monetary policy identification center
around the relatively small amount of information used by low-dimensional VARs. To
conserve degrees of freedom, standard VARs rarely employ more than six to eight
variables.
1
This small number of variables is unlikely to span the information sets used
by actual central banks, who are known to follow literally hundreds of data series, or by
the financial market participants and other observers. The sparse information sets used in
typical analyses lead to at least two potential sets of problems with the results. First, to
the extent that central banks and the private sector have information not reflected in the
VAR analysis, the measurement of policy innovations is likely to be contaminated. A
standard illustration of this potential problem, which we explore in this paper, is the Sims
(1992) interpretation of the so-called “price puzzle”, the conventional finding in the VAR
literature that a contractionary monetary policy shock is followed by a slight increase in
the price level, rather than a decrease as standard economic theory would predict. Sims’s
explanation for the price puzzle is that it is the result of imperfectly controlling for
information that the central bank may have about future inflation. If the Fed
systematically tightens policy in anticipation of future inflation, and if these signals of
future inflation are not adequately captured by the data series in the VAR, then what
appears to the VAR to be a policy shock may in fact be a response of the central bank to
new information about inflation. Since the policy response is likely only to partially
offset the inflationary pressure, the finding that a policy tightening is followed by rising
1
Leeper, Sims, and Zha (1996) increase the number of variables included by applying Bayesian priors, but
their VAR systems still typically contain less than 20 variables.
3
prices is explained. Of course, if Sims’ explanation of the price puzzle is correct, then all
the estimated responses of economic variables to the monetary policy innovation are
incorrect, not just the price response.
A second problem arising from the use of sparse information sets in VAR
analyses of monetary policy is that impulse responses can be observed only for the
included variables, which generally constitute only a small subset of the variables that the
researcher and policymakers care about. For example, both for policy analysis and model
validation purposes, we may be interested in the effects of monetary policy shocks on
variables such as total factor productivity, real wages, profits, investment, and many
others. Another reason to be interested in the responses of many variables is that no
single time series may correspond precisely to a particular theoretical construct. The
concept of “economic activity”, for example, may not be perfectly represented by
industrial production or real GDP. To assess the effects of a policy change on “economic
activity”, therefore, one might wish to observe the responses of multiple indicators
including, say, employment and sales, to the policy change.
2
Unfortunately, as we have
already noted, inclusion of additional variables in standard VARs is severely limited by
degrees-of-freedom problems.
Is it possible to condition VAR analyses of monetary policy on richer information
sets, without giving up the statistical advantages of restricting the analysis to a small
number of series? In this paper we consider one approach to this problem, which
combines the standard VAR analysis with factor analysis.
3
Recent research in dynamic
2
An alternative is to treat “economic activity” as an unobserved factor with multiple observable indicators.
That is essentially the approach we take in this paper.
3
Lippi and Reichlin (1998) consider a related latent factor approach that also exploits the information from
a large data set. Their approach differs in that they identify the common factors as the structural shocks,
